Data collection and management system, data collection and management method, terminal, and management apparatus

ABSTRACT

A data collection and management system includes: a terminal(s) that transmits measured data; and a management apparatus that receives and manages the measured data transmitted from the terminal(s). The management apparatus includes a notification unit that notifies the terminal(s) of transmission cost information indicating a cost incurrable upon transmitting measured data to the management apparatus by the terminal(s). The terminal(s) includes: a calculation unit that calculates an information value indicating a value of measured data as information; and a transmission control unit that determines whether to transmit measured data to the management apparatus based on the information value and the transmission cost information.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is a national stage of International Application No.PCT/JP2014/058348 filed Mar. 25, 2014, claiming priority based onJapanese Patent Application No. 2013-063579 filed Mar. 26, 2013, theentire contents of which are incorporated herein by reference.

TECHNICAL FIELD

The present invention relates to a data collection and managementsystem, a data collection and management method, a terminal, and amanagement apparatus. In particular, it relates to a data collection andmanagement system, a data collection and management method, a terminal,and a management apparatus which relate to data that is collected via anetwork and is accumulated.

BACKGROUND

In recent years, research and development has been actively done on useof data, which is collected from various sensors in a system as typifiedby a machine-to-machine (M2M) system, for various applications. In anM2M system, data obtained from various sensors is collected in atime-series manner, and the collected data is transmitted to amanagement server via a network, in particular, via a mobile network.The management server manages the data collected from various sensors(time-series data) so that various applications can use the data.Currently, such time-series data uniformly consumes resources (forexample, the network band and the resources of the management server).

However, such data has different importance, depending on theapplication or the situation in which the data is used. For example,there is an application that acquires the tracks of moving vehicles byregularly acquiring information about the positions of the vehicles.This application can analyze the traffic congestion status or the likeat a certain location. For example, when a vehicle drives on anexpressway at a constant speed, many points can accurately beinterpolated from rough data points. Namely, data other thanrepresentative points (data points) has a low value (not important). Inaddition, since vehicles drive at a very low speed in an area where atraffic congestion is caused, measurement of data at short intervals isnot necessary. However, when a vehicle drives on complicated streets orrepeatedly stops and goes, it is difficult to accurately detect theposition of the vehicle without many data near each change point.Therefore, even when the same type of data is used (for example, vehicleposition information), the data could have different importance,depending on the application or the situation in which the data is used.Namely, the importance of the data changes dynamically.

Patent Literature (PTL) 1 discloses a technique in which vehicle datacollected by an on-vehicle portable terminal equipment is classifiedaccording to type. In PTL 1, the transmission timing is adjusted on thebasis of a predetermined priority. For example, while data relating tosafety of a vehicle is transmitted promptly to a management server,non-urgent data such as fault diagnosis information is transmitted afterthe vehicle is stopped. PTL 2 discloses a technique in which two queuesare prepared, each being provided with a priority in advance, and thetransmission timing is adjusted. In addition, PTL 3 discloses aconfiguration in which sensor data is stored in a buffer on the basis ofa type of the data, a predetermined calculation is performed, andwhether to transmit the data is determined. In addition, PTL 4 disclosesa technique in which, on the basis of a fluctuation range of detectiondata, the importance of data acquired from a sensor is determined andthe data transmission frequency is changed.

-   PTL 1: Japanese Patent Kokai Publication No. JP2011-076322A-   PTL 2: Japanese Patent Kokai Publication No. JP2010-026815A-   PTL 3: Japanese Patent Kokai Publication No. JP2011-244406A-   PTL 4: Japanese Patent Kokai Publication No. JP2011-188338A

SUMMARY

The disclosure of each of the above PTLs is incorporated herein byreference thereto. The following analysis has been given by the presentinventors.

As described above, even when the same type of data is used, the datacould have different importance, depending on the application or thesituation in which the data is used. Namely, the importance of the datachanges dynamically. According to the techniques disclosed in PTLs 1 and2, priorities need to be defined in advance. Namely, according to thetechniques disclosed in PTLs 1 and 2, the data transmission timing isadjusted on the basis of static settings. Therefore, the techniquesdisclosed in PTLs 1 and 2 cannot accommodate cases where data is of thesame type and of different importance (cases where the importancechanges dynamically), for example. Likewise, the technique disclosed inPTL 3 cannot accommodate the dynamic change of the importance of data.As a result, if any one of the techniques disclosed in PTLs 1 to 3 isapplied to a system, the system has a problem with its low resource useefficiency. In addition, the technique disclosed in PTL 4 is a techniquein which, when collision of data could occur, the data transmissionfrequency is changed on the basis of the importance of the data. Namely,data having higher importance is processed preferentially. However,according to the technique disclosed in PTL 4, data is still transmittedat predetermined intervals, whether the importance of the data is highor low. Thus, in a receiving apparatus that receives the data, there isno change regarding the resources consumed by the data. Namely, as longas the data receiving apparatus processes the values of these dataequally, the resource use efficiency is low.

The above problem is attributable to uniformly uploading data collectedby terminals and the like to a management server via a network such as amobile network and storing the data in a database. Namely, since thedata collected by the terminals and the like is processed as data havingthe same value, a lot of network resources or calculation resources arewasted. Depending on an application used (analytical processing of anytype), the data collected by the terminals includes, for example, manynon-urgent data or many redundant data that affects the analysis resultlittle. However, data having high importance and data having lowimportance (non-urgent data or redundant data) uniformly consumeresources.

The present invention has been made in view of the above circumstances,and it is an object of the present invention to provide a datacollection and management system, a data collection and managementmethod, a terminal, and a management apparatus that contribute toimproving the resources use efficiency.

According to a first aspect of the present invention, there is provideda data collection and management system, including: a terminal(s) thattransmits measured data; and a management apparatus that receives andmanages the measured data transmitted from the terminal(s), wherein themanagement apparatus includes a notification unit that notifies theterminal(s) of transmission cost information indicating a costincurrable upon transmitting measured data to the management apparatusby the terminal(s), wherein the terminal(s) includes: a calculation unitthat calculates an information value indicating a value of measured dataas information; and a transmission control unit that determines whetherto transmit measured data to the management apparatus based on theinformation value and the transmission cost information.

According to a second aspect of the present invention, there is provideda data collection and management method, including: causing aterminal(s) to transmit measured data to a management apparatus; causinga management apparatus to notify the terminal(s) of transmission costinformation indicating a cost incurrable upon transmitting measured datato the management apparatus by the terminal(s); calculating aninformation value indicating a value of measured data as information;and causing the terminal(s) to determine whether to transmit measureddata to the management apparatus based on the information value and thetransmission cost information.

This method is associated with certain machines, namely, with theterminal(s) and the management apparatus.

According to a third aspect of the present invention, there is provideda terminal, including: a calculation unit that calculates an informationvalue indicating a value of measured data as information; and atransmission control unit that determines whether to transmit measureddata to a management apparatus based on: transmission cost informationindicating a cost incurrable upon transmitting the measured data by theterminal to the management apparatus that manages measured data; and theinformation value.

According to a fourth aspect of the present invention, there is provideda management apparatus, including: a reception unit that receivesmeasured data transmitted from a terminal(s); and a notification unitthat notifies the terminal(s) of transmission cost informationindicating a cost incurrable upon transmitting measured data to themanagement apparatus by the terminal(s).

According to the above aspects of the present invention, a datacollection and management system, a data collection and managementmethod, a terminal, and a management apparatus that contribute toimproving the resource use efficiency are provided.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an outline of an exemplary embodiment.

FIG. 2 illustrates an example of a configuration of a data collectionand management system according to a first exemplary embodiment.

FIG. 3 illustrates an example of an internal configuration of a terminal10.

FIG. 4 illustrates an example of an internal configuration of a server20.

FIGS. 5A-5C illustrate examples of attribute classification informationstored in a data structure information management unit 102.

FIG. 6 illustrates an example of calculation of an information value.

FIG. 7A illustrates an example of change of an information value 401 anda transmission cost 402 over time and FIG. 7B illustrates an example ofchange of corresponding transmission probability 403 over time.

FIG. 8 is a flowchart illustrating an example of an operation performedby the terminal 10.

FIGS. 9A and 9B illustrate concept of data estimation performed by adata estimation unit 204.

FIG. 10 illustrates data processing performed by the server 20.

FIG. 11 is a flowchart illustrating an example of an operation performedby the server 20.

FIG. 12 illustrates an example of a configuration of a vehicle datacollection and management system according to a second exemplaryembodiment.

FIG. 13 illustrates an example of an internal configuration of a controlunit 801 included in a mobile terminal 702.

PREFERRED MODES

First an outline of an exemplary embodiment will be described withreference to FIG. 1. In the following outline, various components aredenoted by reference characters for the sake of convenience. Namely, thefollowing reference characters are merely used as examples to facilitateunderstanding of the present invention. The description of the outlineis not intended to impose any limitations on the present invention.

As described above, the resource use efficiency in a data collection andmanagement system that manages data collected from terminals needs to beimproved.

To this end, for example, a data collection and management systemillustrated in FIG. 1 is provided. The data collection and managementsystem illustrated in FIG. 1 includes a terminal 1 that transmitsmeasured data and a management apparatus 2 that receives and managesmeasured data transmitted from the terminal 1. The management apparatus2 includes a notification unit 901 that notifies the terminal 1 oftransmission cost information indicating a cost incurrable upontransmitting measured data to the management apparatus 2 by the terminal1. The terminal 1 includes a calculation unit 902 that calculates aninformation value indicating a value of measured data as information anda transmission control unit 903 that determines whether to transmitmeasured data to the management apparatus 2 based on the informationvalue and the transmission cost information.

With the above configurations and functions, in the data collection andmanagement system illustrated in FIG. 1, data having higher importancepreferentially consumes the system resources (for example, the networkband between the terminal 1 and the management apparatus 2 and thecalculation resources of the management apparatus 2). More specifically,before transmitting measured data, the terminal 1 calculates the value(information value) of the measured data. If the value of the measureddata is low or if the cost required for transmission of the measureddata is high, the terminal 1 does not transmit the measured data. As aresult, it is possible to improve the resource use efficiency withoutdeteriorating the accuracy in various types of analysis and the likeperformed by using measured data managed by the management apparatus 2.

Hereinafter, specific exemplary embodiments will be described in detailwith reference to drawings.

[First Exemplary Embodiment]

A first exemplary embodiment will be described in detail with referenceto drawings.

FIG. 2 illustrates an example of a configuration of a data collectionand management system according to the present exemplary embodiment. Asillustrated in FIG. 2, the data collection and management systemincludes a plurality of terminals 10-1 to 10-n (n is a positiveinteger), a server 20, and a plurality of measurement apparatuses 30-1to 30-m (m is a positive integer) each of which includes a sensor or thelike. Hereinafter, any one of the terminals 10-1 to 10-n will bereferred to as a “terminal 10” unless the terminals 10-1 to 10-n need tobe distinguished from each other. Likewise, any one of the measurementapparatuses 30-1 to 30-m will be referred to as a “measurement apparatus30” unless the measurement apparatuses 30-1 to 30-m need to bedistinguished from each other.

An individual measurement apparatus 30 transmits measured data obtainedfrom its internal sensor to a corresponding terminal 10 in response to arequest from the corresponding terminal 10. The terminals 10 areconnected to the server 20 via a network 40. The terminals 10 outputmeasured data to the server 20. The server 20 receives the measured datatransmitted from the respective terminals 10 and manages the receivedmeasured data. In addition, the server 20 outputs control signals to theterminals 10. The configuration illustrated in FIG. 2 is only anexample, and therefore the configuration of the system is not limitedthereto. For example, a measurement apparatus 30 and a terminal 10 maybe stored in the same casing.

FIG. 3 illustrates an example of an internal configuration of a terminal10. As illustrated in FIG. 3, the terminal 10 includes a data collectioninterface 101, a data structure information management unit 102, a datacollection unit 103, a data management unit 104, a control signalmanagement unit 105, an information value estimation unit 106, a datatransmission control unit 107, and a communication unit 108.

The data collection interface 101 is a control interface connected tothe corresponding measurement apparatuses 30. The data collectioninterface 101 collects measured data from the measurement apparatuses30. The measured data collected by the data collection interface 101 isdata collected by various sensors such as about the position of theterminal 10, the acceleration of the terminal 10 when the terminal 10moves, and the ambient temperature or humidity around the terminal 10.The format of the measured data collected by the data collectioninterface 101 is not particularly limited. For example, the measureddata may be binary data obtained by converting data outputted fromvarious sensors into digital signals or may be text data.

The data collection unit 103 collects the measured data from the datacollection interface 101. By referring to information stored in the datastructure information management unit 102, the data collection unit 103determines how the measured data needs to be handled. More specifically,if the measured data is determination data which will be describedbelow, the data collection unit 103 outputs the measured data(determination data) to the data management unit 104 and the informationvalue estimation unit 106. However, if the measured data is not suchmeasured data (if the measured data is dependent data which will bedescribed below), the data collection unit 103 outputs the measured datato the data management unit 104, not to the information value estimationunit 106. In this way, depending on the determination made on the basisof the information stored in the data structure information managementunit 102, the data collection unit 103 outputs the measured data to thedata management unit 104 and the information value estimation unit 106.

The data management unit 104 stores and manages measured data.

The data structure information management unit 102 stores attributeclassification information that is provided for determination of aninformation value of data. The attribute classification informationstored in the data structure information management unit 102 is set byan administrator of the system in advance. The attribute classificationinformation will be described in detail below.

The control signal management unit 105 manages a control signaltransmitted from the server 20. The control signal management unit 105outputs a calculation result obtained from a control signal transmittedfrom the server 20 to the data transmission control unit 107 astransmission cost for the terminal 10. The control signal transmittedfrom the server 20 and the transmission cost will be described in detailbelow.

The information value estimation unit 106 estimates the value ofmeasured data collected by the data collection unit 103 on the basis ofmeasured data in the past. In this way, the information value estimationunit 106 calculates the information value of the measured data. Theinformation value calculated by the information value estimation unit106 is a value index parameter that is used to determine whether totransmit the measured data to the server 20. The information valueestimation unit 106 outputs the calculated information value to the datatransmission control unit 107.

The data transmission control unit 107 determines whether to transmitthe measured data to the server 20 on the basis of the information valuecalculated by the information value estimation unit 106 and thetransmission cost outputted from the control signal management unit 105.If the data transmission control unit 107 determines to transmit themeasured data to the server 20, the data transmission control unit 107acquires the measured data from the data management unit 104 andtransmits the measured data to the server 20 via the communication unit108.

FIG. 4 illustrates an example of an internal configuration of the server20. As illustrated in FIG. 4, the server 20 includes a communicationunit 201, a control signal management unit 202, a data reception unit203, a data estimation unit 204, a data management unit 205, and adatabase 206.

The communication unit 201 is means for mutually communicating with theterminals 10. The communication unit 201 receives measured datatransmitted from the terminals 10 and transmits control signals to theterminals 10.

The data reception unit 203 receives the measured data transmitted fromthe terminals 10. The data reception unit 203 outputs the receivedmeasured data to the data management unit 205.

The data management unit 205 registers the received measured data in thedatabase 206. In addition, the data management unit 205 is means thatsupports data access (reading, writing, updating, etc.) from the outside(for example, from an apparatus that analyzes measured data). To managedata, the database 206 can be configured as a relational database.However, another configuration may alternatively be used.

The data estimation unit 204 is means for estimating unreceived measureddata from the measured data (existing measured data) registered in thedatabase 206.

The control signal management unit 202 is means for generating controlsignals from the existing measured data. An individual control signalincludes information that is necessary for a terminal 10 to perform datatransmission control. The control signal management unit 202 transmitsthe control signals to the terminals 10 via the communication unit 201.The control signal management unit 202 may regularly generate andtransmit the control signals or on demand from the outside such as froman administrator. The following description will be made assuming thatthe control signal management unit 202 regularly generates and transmitscontrol signals at preset intervals.

Each unit included in an individual terminal 10 may be realized by acomputer program that causes a computer included in the terminal 10 touse hardware of the computer and to perform processing described indetail below.

Next, an operation of each unit included in a terminal 10 will bedescribed.

The terminal 10 transmits measured data acquired from its correspondingmeasurement apparatuses 30 to the server 20. Before transmitting themeasured data, the terminal 10 performs dynamic data transmissioncontrol on the measured data. More specifically, the terminal 10 adjuststiming at which the measured data is transmitted or selects data thatneeds to be transmitted. When performing such operation, the terminal 10uses the attribute classification information stored in the datastructure information management unit 102.

FIGS. 5A-5C illustrate examples of attribute classification informationstored in the data structure information management unit 102. Asillustrated in FIGS. 5A-5C, the data structure information managementunit 102 previously holds a relationship among the measured dataillustrated in FIGS. 5A-5C. Measured data can be classified into one ofthe two attributes of determination data and dependent data.Determination data is a parent node, and dependent data is a child node.These data have a master-servant relationship as illustrated in FIGS.5A-5C. For example, determination data 1 is a parent node of dependentdata 1-1 and 1-2 (see FIG. 5A). Measured data is classified into anattribute, namely, into determination data or dependent data. Thedependent data is dependent on at least one determination data. The datastructure information management unit 102 holds a data relationshipapplied to the measured data obtained from the corresponding measurementapparatuses 30. For example, position information indicating theposition of the terminal 10 can be considered as determination data. Incontrast, for example, the movement speed of the terminal 10 can beconsidered as dependent data that is dependent on the determinationdata.

The data collection unit 103 collects various data measured by thecorresponding measurement apparatuses 30 via the data collectioninterface 101. The data collection unit 103 may collect the measureddata at predetermined intervals. Alternatively, the measured data may beuploaded by the measurement apparatuses 30 asynchronously. In eitherway, the data collection unit 103 uses regularly-provided datacollection time slots to keep track of time at which measured data iscollected. By using time slot numbers, the data collection unit 103manages the measured data. The time slot numbers are synchronizedbetween an individual terminal 10 and the server 20. The synchronizationbetween an individual terminal 10 and the server 20 is achieved bycausing the terminal 10 to synchronize with the server 20 when theterminal 10 is started.

The data collection unit 103 stores measured data that is collected atthe same time slot in the data management unit 104, by using a datastructure made in view of the attribute classification information thatis managed by the data structure information management unit 102 andthat has a master-servant relationship. The data structure is forassociating dependent data stored in the data management unit 104 withdetermination data so that the dependent data can be extracted by usingthe determination data as a key. If the data collection unit 103 cannotacquire determination data serving as a parent of dependent data withinthe same time slot, the data collection unit 103 associates the nextdetermination data to be acquired with the dependent data.

The control signal management unit 105 manages a control signal receivedfrom the server 20. The control signal transmitted from the server 20represents transmission cost information used by the data transmissioncontrol unit 107 to determine whether to transmit measured data to theserver 20. In addition, the control signal is a processed and shapedsignal so that, after receiving transmission cost information, theterminal can calculate a transmission cost required to transmit thecorresponding measured data.

For example, if the control signal management unit 105 acquires acontrol signal about the position, the control signal management unit105 manages transmission cost information in which the transmissionposition is used as the key.

If the data collection unit 103 acquires new measured data and if theacquired measured data is determination data, the data collection unit103 outputs the measured data and the measured time slot number to theinformation value estimation unit 106.

The information value estimation unit 106 includes a prediction moduleand estimates the information value of received measured data fromprediction information. The information value of the measured datarepresents the amount of decrease in prediction performance when themeasured data is assumed to be absent. More specifically, if measureddata can be predicted accurately, the information value of the measureddata is determined to be low. This is because, even when such measureddata that can be predicted accurately does not exist, interpolated datacan be generated from other measured data. Namely, the information valueof measured data estimated by the information value estimation unit 106is an index that quantifies the magnitude of the impact caused bypresence/absence of the measured data. It is desirable that a predictionand estimation module using measured data in the past be used and thatthe information value of measured data be determined by the magnitude ofthe difference between a result obtained by prediction and estimationthat uses the measured data and a result obtained by prediction andestimation that does not use the measured data.

It is only necessary that the prediction module of the information valueestimation unit 106 predicts probability distribution of measured dataat a time slot any time after the next time slot by using measured datathat has already been acquired (measured data in the past). For example,for the prediction module, it is possible to use means that uses arecursive filter as typified by a Kalman filter, so as to estimate astate while sequentially updating an internal state without historyinformation in the past. However, realization of the prediction moduleis not limited to use of a Kalman filter. Prediction using other derivedfilters, time-series data analysis methods, or machine learning isapplicable.

Next, an example of calculation of an information value will bedescribed with reference to FIG. 6. In FIG. 6, the current time slotnumber is tk, and the measured data corresponding to tk is D(tk). Thefollowing example will be described assuming that the data managementunit 104 holds a group of measured data 301 that has been collected upuntil the current time and that the information value estimation unit106 holds, as an estimation range 302, an estimated range in which thecurrent data including a measurement error falls most probably. Inaddition, the estimated and predicted range of data corresponding totime slot number tk+3, which is three time slots after the current timeslot number, will be indicated as an estimated range 303. Under suchcircumstances, the data management unit 104 actually acquires measureddata per time slot, thereby acquiring a group of measured data 304including D(tk+1) to D(tk+3).

The information value estimation unit 106 updates the estimated range302 each time the information value estimation unit 106 acquiresmeasured data. As a result of the updating of the estimated range, thepredicted and estimated range corresponding to the time slot number tk+3is updated to an estimated range 305. Namely, the estimated range 305 isthe most accurately estimated range obtained by using all the acquiredmeasured data, and the estimated range 303 is the most accuratelyestimated range obtained by using all the measured data that has beenacquired up until the time slot number tk. Thus, the smaller thedifference between the estimated ranges 303 and 305 is, the higher theprediction accuracy becomes. In other words, the smaller the differencebetween the estimated ranges 303 and 305 is, the lower the informationvalue becomes.

This is because, even if the server 20 does not acquire the group ofmeasured data 304, the server 20 can accurately predict the target databy using its prediction function (prediction module) equivalent to thatof the information value estimation unit 106. Namely, such measured datahaving a low information value can be considered to be non-urgentmeasured data.

To quantify the difference between the estimated ranges 303 and 305, thedistance between both of the distributions can be obtained by usingKullback-Leibler divergence DKL. Assuming that the estimateddistribution obtained before the group of measured data 304 is acquiredis Φ1 and the estimated distribution obtained after the group ofmeasured data 304 is acquired is Φ2, the divergence obtained by thegroup of measured data 304 is given by DKL(Φ2//Φ1).

If estimation of data x is indicated by a distribution function F(x),DKL(Φ2//Φ1) can be approximated by a Gaussian function N (μ,Σ) using thecorresponding expected value μ and variance-covariance matrix Σ. Forexample, if target measured data is position information (coordinatevalues on the two-dimensional Euclidean space), μ represents theexpected position and Σ represents uncertainty of the position. If thereare two estimations of Φ1=N (μ1, Σ1) and Φ2=N (μ2, Σ2), the distance tothe estimation Φ2 from the estimation Φ1 can be evaluated by thefollowing mathematical expression (1).

$\begin{matrix}{{{DKL}\left( {{\phi 1}{}{\phi 2}} \right)} = {\frac{1}{2}\left\lbrack {{{Ln}\frac{\underset{1}{\;\sum\;}}{\underset{2}{\;\sum\;}}} - N + {{tr}\left\{ {\sum\limits_{1}^{- 1}\sum\limits_{2}} \right\}} + {\left( {\mu_{2} - \mu_{1}} \right)^{T}{\sum\limits_{1}^{- 1}\left( {\mu_{2} - \mu_{1}} \right)}}} \right\rbrack}} & (1)\end{matrix}$In the above mathematical expression (1), tr(A) represents the trace ofa matrix A, and N represents the dimension number. The larger the valueobtained by the above mathematical expression (1) is, the higher theinformation value becomes. In this way, the magnitude of the informationvalue can be obtained by estimating a state with target measured dataand a state without the target measured data and by calculating thedifference in the distance between predicted and estimated datadistributions determined by both the state estimation results.

Per determination data, the data transmission control unit 107determines timing at which the corresponding measured data istransmitted, by using the corresponding information value calculated bythe information value estimation unit 106 and the correspondingtransmission cost obtained by the control signal management unit 105.The data transmission control unit 107 may collectively transmitmeasured data that has been accumulated up until the current time sincemeasured data is transmitted last (stop/go control). Alternatively, thedata transmission control unit 107 may select and transmit importantmeasured data. The importance of measured data can be determined bycausing the information value estimation unit 106 to calculate thesubset of the measured data that has been accumulated up until thecurrent time and to calculate the information value of the subset.However, for simplicity, the present exemplary embodiment will bedescribed assuming that the data transmission control unit 107 transmitsthe measured data that has been accumulated up until the current timesince measured data is transmitted last.

Before transmitting measured data, the data transmission control unit107 gives an importance attribute to the measured data, so that theserver 20 can distinguish measured data having high importance frommeasured data having low importance. In the following description,measured data having high importance is provided with an importanceattribute “M.” In addition, among the untransmitted measured data,measured data having low importance is provided with an importanceattribute “L.”

The measured data provided with an importance attribute “M” is importantwhen used. Whether measured data needs to be provided with an importanceattribute “M” is determined by a policy of a designer (a policy fordetermining the importance attribute is determined in advance). Forexample, there are cases in which a terminal 10 determines to transmitmeasured data to the server 20 after a relatively long period of timeelapses since the terminal 10 transmitted measured data last. In suchcases, in accordance with one conceivable policy, the terminal 10 givesan importance attribute “M” to the measured data corresponding to thetiming (time slot) at which the terminal 10 determines to transmit themeasured data or to the measured data at the timing (time slot)immediately before the timing (time slot). This is because, since theterminal 10 has not transmitted any measured data for a long time, theserver 20 is caused to promptly detect the magnitude of the change fromthe measured data at the previous timing (time slot). The measured dataother than the measured data provided with an importance attribute “M”is provided with an importance attribute “L.”

The data transmission control unit 107 determines whether to transmitmeasured data on the basis of the magnitude of the information value Vand a transmission cost P. For example, the data transmission controlunit 107 may perform statistical processing on the magnitude of theinformation value V and the transmission cost P and calculatetransmission probability. In this way, depending on the transmissionprobability, the data transmission control unit 107 determines whetherto transmit the measured data. Normally, the larger the magnitude of theinformation value V is and the smaller the transmission cost P is, thehigher the transmission probability needs to become. Thus, thetransmission probability is represented by a monotonically increasingfunction with respect to V and a monotonically decreasing function withrespect to P. For example, transmission probability G (V, P) can becalculated by the following mathematical expression (2). However, thecalculation method of the transmission probability is not limited to themathematical expression (2).

$\begin{matrix}{{G\left( {V,P} \right)} = \frac{1}{1 + {\left( {1 - \alpha} \right)e^{{- \beta}\frac{V}{P}}}}} & (2)\end{matrix}$In the mathematical expression (2), α and β are adjustable parameters.The adjustable parameters α and β may be predetermined values or may bevalues that can dynamically be changed by the terminal 10 or the server20. By dynamically changing the adjustable parameters α and β, the datatransmission control unit 107 can achieve further detailed dynamicadjustment of the transmission timing.

FIG. 7A illustrates an example of change of an information value 401 anda transmission cost 402 over time and FIG. 7B illustrates an example ofchange of corresponding transmission probability 403 over time.Normally, since the prediction accuracy deteriorates over time, theinformation value is improved. In contrast, since the transmission cost402 is a value determined by a control signal acquired from the server20, the transmission cost 402 does not have any particular tendency (thetransmission cost 402 can be any value). As illustrated in FIGS. 7A and7B, the higher the information value 401 is and the lower thetransmission cost 402 is, the higher the transmission probability 403becomes. In this way, the terminal 10 determines whether to transmitmeasured data on the basis of the transmission probability, which isrepresented by a decreasing function with respect to the transmissioncost and an increasing function with respect to the information value.Thus, the data transmission control unit 107 determines whether totransmit measured data on the basis of the transmission probability 403.

Alternatively, instead of determining whether to transmit measured dataon the basis of the determination of the transmission probability, thedata transmission control unit 107 may optimize the transmission timingon the basis of the relationship between the information value and thetransmission cost. For example, the transition of the prediction of themagnitude of the information value V from the current time and thetransition of the prediction of the transmission cost P may becalculated. In such case, the difference (V−γP) between the calculatedtransitions of the predictions or the time (time slot) at which astatistic such as V/P is maximized is searched for. In this way, themeasured data is transmitted at the found time. An appropriatecoefficient value is selected for γ. A more specific case in which thecontrol signal management unit 105 receives transmission costinformation P[X] at a location as a control signal will be described. Inthis case, when predicting a time-varying movement track X[t], thetransmission cost at certain time t can be calculated as P[X[t]]. Ifchange of the magnitude of the information value V over time can bepredicted, the prediction result can be used. However, if change of themagnitude of the information value V over time cannot be predicted, thecurrent magnitude of the information value V is used.

Alternatively, normally, since the magnitude of the information value Vtends to increase over time (the prediction accuracy deteriorates overtime), by using an appropriate increasing function with respect to time,the time (time slot) at which a statistic such as the above differenceis maximized can be calculated. However, normally, even when thetransmission cost of measured data is high, it is desirable that themeasured data be transmitted at certain intervals in order to maintain acertain level of quality. In such case, it is desirable that theterminal be caused to transmit measured data after a certain number oftime slots since measured data is transmitted last.

The terminals 10 (the respective control signal management units 105)can be configured to receive regularly updated control signals if thedownlink network band from the server 20 to the terminals 10 is notlimited. However, if use of the downlink network band also needs to bereduced, for example, the server 20 may transmit control signals toterminals 10 when measured data is uploaded by the terminals 10. This isbecause it is often the case that the data collection and managementsystem has sufficient resources and the downlink network band is nottight when measured data is transmitted from terminals 10.

Next, an operation of a terminal 10 will be described.

FIG. 8 is a flowchart illustrating an example of an operation performedby a terminal 10.

In step S101, the data collection unit 103 collects measured data. Thedata collection unit 103 registers the collected data in the datamanagement unit 104 as data structured on the basis of the correspondingattribute (determination data or dependent data).

In step S102, the information value estimation unit 106 calculates aninformation value by using determination data.

In step S103, the data transmission control unit 107 determines whetherto transmit the measured data on the basis of the transmission costobtained from the control signal management unit 105 and the informationvalue calculated by the information value estimation unit 106.

If the data transmission control unit 107 determines to perform “datatransmission” (Yes in step S103), the data transmission control unit 107transmits the determination data and the corresponding dependent data tothe server 20, and the processing returns to step S101 (step S104).Otherwise (No in step S103), the data is accumulated within the terminal10, and the processing returns to step S101 (step S105).

Next, an operation of an individual unit included in the server 20 willbe described.

The server 20 holds measured data transmitted from the terminals 10 inthe database 206 and manages the measured data so that variousapplications can use the measured data. By causing an individualterminal 10 to perform data transmission control on measured data,marking information about an importance attribute is added to themeasured data. The server 20 preferentially stores measured data havinghigher importance, in accordance with such marking information about animportance attribute. Namely, depending on the importance of themeasured data transmitted from the terminals 10, the server 20preferentially allocates resources (for example, a hard disk). In thisway, the server 20 preferentially stores measured data, which has beentransmitted from the terminals 10 preferentially over other measureddata. Consequently, since the server 20 intermittently stores measureddata, when managing the measured data, the server 20 needs tointerpolate or estimate other measured data. In addition, the server 20transmits control signals to the terminals 10.

The data reception unit 203 acquires measured data uploaded by anindividual terminal 10 via the communication unit 201 and outputs themeasured data to the data management unit 205. The data management unit205 registers the measured data acquired from an individual terminal 10in the database 206. When registering the measured data, in addition tothe measured data and the measurement time of the measured data, thedata management unit 205 also stores a time slot number, a typeindicating determination or dependent data, and an importance attribute,per terminal (per terminal identifier).

The control signal management unit 202 notifies an individual terminal10 of transmission cost information by transmitting a control signal tothe terminal 10. The control signal management unit 202 regularlygenerates and updates a control signal in view of the current dataacquisition status, the load status of the server 20, the prediction andestimation status of unreceived measured data, and the like. Anindividual terminal 10 needs the control signal to calculate atransmission cost. What is used as the transmission cost depends on apolicy of a designer. For example, the following description will bemade assuming that information about the density of terminals 10 at anindividual area is used as the control signal so that a plurality ofterminals 10 do not simultaneously transmit measured data. In an areawhere terminals 10 densely exist, many measured data are transmittedfrom the area. Thus, the control signal management unit 202 generates acontrol signal so that the transmission cost is increased accordingly.Thus, the transmission cost information is an index that reflects howintensely the measured data uploaded by terminals 10 consumes resources.The control signal management unit 202 manages the transmission costinformation so that the value corresponding to the transmission costinformation is increased as concentration degree of resources amountincreases.

More specifically, a field (map) is divided into small areas, and thenumber of terminals N[i] in an area i (hereinafter, i is an integer) iscalculated. In addition, for example, an appropriate upper limit C isdetermined, and a value defined within [0, 1] such as min (1, N[i]/C) isused as the control signal (transmission cost) in the area i. If thereis no problem with the data transmission amount, the server 20 maytransmit a control signal relating to all the areas to each terminal 10.Alternatively, using the current position of the control signaltransmission target terminal 10 as a center, an appropriate radius maybe determined, and a control signal relating to the areas included inthe circle formed by the radius may be transmitted.

The control signal management unit 202 may transmit a control signalregularly. Alternatively, when the data reception unit 203 receivesmeasured data, the control signal management unit 202 may transmit acontrol signal (updated control signal) to the terminal 10. For example,when a terminal 10 receives density information as a control signal, ifthe density is high, the terminal 10 determines that the transmissioncost is high. If the terminal 10 determines that the transmission costis high, the transmission probability is decreased. Thus, transmissionof measured data from such a dense area is reduced. As a result, aplurality of terminals 10 less compete for consumption of the resources.

In many cases, the data managed by the server 20 is measured data thathas been intermittently collected. Namely, while measured data havinghigh importance (measured data having an importance attribute “M”) iscollected promptly, acquisition of measured data having low importance(measured data having an importance attribute “L”) depends on when theterminal 10 transmits the measured data. For example, if a terminal 10performs stop/go control in which the terminal 10 transmits all themeasured data accumulated since measured data is transmitted last, theserver 20 acquires the past measured data collectively at thattransmission timing. In addition, if a terminal 10 performs a complexdetermination operation on the importance of measured data andselectively transmits data having high importance, the server 20 cannotdetermine when the terminal 10 transmits measured data having lowimportance.

Thus, for example, if the server 20 needs untransmitted measured datawhen analyzing measured data, the server 20 causes the data estimationunit 204 to operate on-demand and estimate necessary data. If anestimation result is not sufficiently accurate, the data estimation unit204 may collect necessary measured data from a relevant terminal via thecommunication unit 201.

The data estimation unit 204 uses the same estimation algorithm as thatused by an individual terminal 10 for calculating an information value.In addition, instead of discarding measured data that has been estimatedonce, the data estimation unit 204 adds an importance attribute “E” tothe measured data and registers the measured data in the database 206.By adding an importance attribute “E,” the data estimation unit 204explicitly indicates that the data is estimated information. If the samemeasured data is accessed next time, the data management unit 205 reusesthe estimated measured data unless there are some special reasons.

For example, specific cases will be described with reference to FIGS. 9Aand 9B. Assuming that measured data 501 is arranged in a time-seriesmanner and a request for data at a future time slot s1 is made, the dataestimation unit 204 uses measured data in the past to generate anestimated value 502 at the time slot s1 (see FIG. 9A).

If a request for data at a past time slot s2 is made, the dataestimation unit 204 can also estimate the requested measured data. Suchcase will be described with reference to FIG. 9B. In FIG. 9B, whilethere is measured data 503, the server 20 has not received informationat a time slot in the past time slots. Even when such unreceivedmeasured data at the time slot s2 is requested, the data estimation unit204 can estimate an estimated value 504 at the time slot s2 by using themeasured data 503 that has already been acquired.

Normally, with new measured data, more accurate estimation can beperformed. Thus, if higher accuracy is needed, the data estimation unit204 may be configured to hold information indicating whether newmeasured data has been inputted since the last data estimationoperation. In this way, when receiving a data access request requiringhigh-precision estimation, the data estimation unit 204 discardsestimated data and generates newly estimated data.

It is desirable that better access performance (for example, the accessrate) be provided to measured data having high importance (importanceattribute “M”), compared with estimated data (importance attribute “E”)and measured data (importance attribute “L”) transmitted belatedly. Forexample, it is desirable that a database including an index for theimportance attributes be configured so that measured data having animportance attribute “M” can be acquired promptly. Alternatively,another database may separately be configured in which only the measureddata having an importance attribute “M” is stored.

Next, a series of operations, including the acquisition of measureddata, estimation of measured data, and overwriting of measured data,will be described with reference to FIG. 10. The following descriptionwill be made by using transition of position information as an example.Terminal-side data 620 illustrated in FIG. 10 is a sequence of positioninformation estimated points. Data classified into measured data havingan importance attribute “M” and measured data having an importanceattribute “L” are transmitted to the server 20. First, among theterminal-side data 620, promptly transmitted data 601 and 602 having animportance attribute “M” are sequentially transmitted as server-sidedata 630. Thus, the server 20 stores priority data 631 only and performslinear interpolation for the other data.

Next, if data at a time slot between measured data having an importanceattribute “M” is requested, the server 20 performs data estimation togenerate measured data (having an importance attribute “E”). In FIG. 10,interpolated data 621 has been generated. FIG. 10 illustrates an examplein which the server 20 performs data estimation through linearinterpolation, and therefore, the corresponding measured data areconnected by a supplementary line. Next, if the measured data at thecorresponding time slot is transmitted belatedly from the terminal, theserver 20 performs data updating 633 in response to the data uploading.In this data updating 633, the server 20 overwrites the interpolateddata 621 with the received data (measured data) having an importanceattribute “L.” The server 20 performs like processing on the othermeasured data and estimates data on-demand. In this way, the server 20can update information when measured data is updated by an individualterminal 10.

If the server 20 needs an actually measured value from a terminal 10,the server 20 may explicitly request the terminal 10 to transmit thenecessary measured data on-demand, instead of estimating the data. Thisis performed only in the data updating 633 in response to datauploading.

Each unit included in the server 20 may be realized by a computerprogram that causes a computer included in the server 20 to use hardwareof the computer and to perform processing described in detail below.

Next, an operation of the server 20 will be described.

FIG. 11 is a flowchart illustrating an example of an operation performedby the server 20. The operation of the server 20 illustrated in FIG. 11is merely an example. Thus, for example, the processing execution orderis not limited. In addition, individual processing does not need to beperformed linearly. For example, the measured-data reception processing(step S201, step S202) and the control signal transmission processing(step S205, step S206) may be performed in parallel.

In step S201, the data reception unit 203 determines whether the server20 has received measured data from a terminal 10. If the server 20 hasreceived measured data (Yes in step S201), the data reception unit 203stores the measured data in the database 206 via the data managementunit 205 (step S202).

In step S203, the data estimation unit 204 determines whether estimationof measured data is needed. If estimation of measured data is needed(Yes in step S203), the data estimation unit 204 performs dataestimation by using data stored in the database 206 (step S204).

In step S205, the control signal management unit 202 determines whethera condition(s) for transmitting a control signal is met. If such acondition(s) is met (Yes in step S205), the control signal managementunit 202 transmits a control signal to an individual terminal 10 via thecommunication unit 201 (step S206). Next, the control signal managementunit 202 calculates transmission cost information (step S207).

As described above, in the data collection and management systemaccording to the present exemplary embodiment, an individual terminal 10selectively transmits measured data having high importance to the server20, and the server 20 includes a function corresponding to selection andtransmission of measured data having high importance. An individualterminal 10 classifies collected measured data into determination dataor data dependent thereon. If the measured data is determined to bedetermination data, the terminal 10 determines whether to transmit themeasured data on the basis of the importance thereof. In this way, theterminal 10 enables dynamic upload control (dynamic data transmissioncontrol). The importance is determined on the basis of how much ananalysis result obtained by the server 20 is affected. Typically, anindex that represents whether interpolation can be performed byprediction is used. Such determination data that has been determined tobe important through the importance determination processing and thecorresponding dependent data are promptly transmitted to the server 20.In contrast, if determination data that has been determined to benon-urgent through the importance determination processing and thecorresponding dependent data are temporarily stored in the terminal 10,and the data is transmitted to the server 20 when the terminals 10 lesscompete for the resources.

The server 20 holds data having high importance and data having lowimportance separately and optimizes a configuration so that theperformance of the access to the data having high importance ismaximized. In addition, for example, when performing any analysis usingmeasured data, there are cases in which the server 20 needs unreceiveddata. In such cases, the server 20 performs data interpolation byperforming data estimation while using data that has already beenreceived. In addition, the server 20 requests terminals 10 to coordinatewith each other by transmitting control signals to the terminals 10. Forexample, a control signal transmitted by the server 20 is an index withwhich a terminal 10 can quantify a transmission cost, such as thedensity of terminals at an individual area. For example, the larger thetransmission cost is, the lower the transmission probability of theterminal 10 becomes. Namely, an individual terminal 10 determineswhether to transmit data on the basis of the balance between thecorresponding information value and transmission cost required totransmit the data.

With the above configurations and the functions of the individualapparatuses, the data collection and management system according to thepresent exemplary embodiment provides the following advantageouseffects.

The first advantageous effect is that the resource use efficiency isimproved. According to the techniques in the above PTLs, since terminalsindependently collect and upload data, the terminals complete for theresources. Namely, the techniques in the above PTLs do not take intoconsideration, for example, the dynamically changing impact of competingfor use of the resources by a plurality of terminals. For example, in anarea where traffic congestion is being caused, many vehicles couldupload data and compete for the network band or the server resources.Thus, according to the techniques in the above PTLs, in order tomaintain at least a certain level of service quality, capacity designneeds to be performed on the basis of the resource use peak value.However, with such countermeasures, a lot of resources are not usedother than in peak hours. Thus, the resource use efficiency isdeteriorated, which is counted as a problem. To avoid this, it isdesirable that non-urgent data having low data importance becollectively transmitted and managed when the resource use rate is low,not when the terminals are competing for the resources.

An individual terminal 10 according to the present exemplary embodimentpreferentially uploads measured data in descending order of importance.As a result, since data having high importance preferentially consumesthe resources, the limited resources can effectively be used. Namely,the resource use efficiency is improved.

The second advantageous effect is that the calculation processing timeis improved. According to the techniques in the above PTLs, whencalculation using accumulated data is performed for analysis or thelike, much calculation time could be needed. This is because thetechniques in the above PTLs equally treat data that affects more anddata that affects less on an analysis result when performing calculationprocessing using accumulated data for analysis or the like. Normally,the calculation time largely depends on the data amount. If a largeamount of data is processed, the calculation time is increasedexponentially. In addition, in many cases, the data processing time canbe expressed by a non-linear function with respect to the data amount.For example, the rate of access to data is nonlinearly decreased as thedata amount is increased. In addition, the time required for complexanalysis such as multivariable analysis or cluster analysis is alsononlinearly increased as the data amount is increased. Thus, it isdesirable that important data be selectively treated and that the dataamount be reduced as much as possible.

In the database 206 of the server 20 according to the present exemplaryembodiment, measured data having high importance is sequentially stored.Since measured data includes a marking representing importance, measureddata used for a target operation can be selected and the effective sizeof the data amount can be reduced. As a result, the calculationprocessing time can be shortened.

The third advantageous effect is that the resource amount necessary forachieving the target performance can be reduced. According to thetechniques in the above PTLs, the resource amount necessary to ensurethe designed performance is increased, and idle and surplus resourcesare increased, which is counted as a problem. For example, if terminalssimultaneously use the resources, since the terminals compete for use ofthe resources, the resource use amount is increased in a burst manner.Thus, to maintain a certain level of quality, it is necessary to designthe resources on the basis of their peak values. However, since such apeak appears only in some time periods of the entire period, largesurplus resources are caused in most time periods.

Since the server 20 according to the present exemplary embodiment canstore received measured data in a database and can process non-urgentdata as a batch when in a low load state, unnecessary concentration onthe network band or database storage processing can be avoided. As aresult, the necessary resource amount can be reduced.

[Second Exemplary Embodiment]

Next, a second exemplary embodiment will be described in detail withreference to the drawings.

A data collection and management system according to the presentexemplary embodiment is a system in which vehicle information such asabout automobiles is collected and managed.

A vehicle such as an automobile includes an OBD2 (On-board diagnostics)system. The OBD2 system is a system for managing vehicle managementinformation such as about the number of revolutions of the engine orvalues of various types of sensors. In the OBD2 system, various types ofdata (which will hereinafter be referred to as OBD data) can be acquiredthrough an OBD interface.

By analyzing the OBD data transmitted from a vehicle, a managementserver analyzes the driving status or malfunction condition of thevehicle. On the basis of the analysis result, the management server canprovide various types of information provision services. Examples of theservices include displaying fuel consumption of a running vehicle,displaying traffic congestion information, providing information about avehicle that has caused an accident to surrounding vehicles. Inaddition, while the present exemplary embodiment will be describedassuming that collected data is OBD data relating to vehicles, varioustypes of sensors may be arranged in infrastructure facilities such astraffic signals, street lights, parking areas, and various types ofstores, and information obtained from these sensors may be used. As aresult, more services of various types can be provided. For example, itis possible to provide a service in which a vehicle is guided to aparking area having an available spot (or a spot that could beavailable) on the basis of a sensor(s) that detects an available spot(s)and information about a running vehicle(s) (information about theposition(s) and speed(s) of a vehicle(s)).

In order to provide analysis at the right timing and accurately inever-changing circumstances (available spots in a parking area, thenumber of vehicles going to such a parking area, etc.), it is necessaryto selectively process important data. The data collection andmanagement system according to the present exemplary embodiment providessuch functions. In the present exemplary embodiment, when the managementserver uses OBD data, an individual vehicle preferentially uploadsimportant information to the management server. In addition, in thepresent exemplary embodiment, a data collection and management systemthat relates to vehicle data will be described. In the data collectionand management system, the peak of an individual resource consumptionamount (the network band, the capacity of a database, etc.) is reducedby preventing vehicles from simultaneously updating data.

FIG. 12 illustrates an example of a configuration of a vehicle datacollection and management system according to the present exemplaryembodiment. As illustrated in FIG. 12, the vehicle data collection andmanagement system includes a vehicle 701 such as an automobile, a mobileterminal 702 such as a smartphone, a mobile network 703, and amanagement server 704.

The vehicle 701 includes a sensor unit 705 that acquires vehicle dataand an OBD interface 706. The sensor unit 705 measures vehicle positiondata by using a GPS (Global Positioning System). In addition, the sensorunit 705 can acquire various types of other data such as about thenumber of revolutions of the engine and about the moving speed. In thepresent exemplary embodiment, vehicle position data and various types ofdata other than the vehicle position data will collectively be referredto as OBD data. In addition, the vehicle position data and the other OBDdata will be determined to be determination data and dependent data,respectively.

The mobile terminal 702 acquires these data (OBD data; determinationdata and dependent data) via the OBD interface 706. More specifically,by transmitting a data transmission request to an ECU (Engine ControlUnit) of the vehicle, the mobile terminal 702 acquires OBD data.

The mobile terminal 702 includes a CPU (Central Processing Unit) and astorage unit in which a program performed by the CPU is stored. Bycausing the CPU to perform a program, the mobile terminal 702 calculatesthe information value of the position information while regularlyacquiring vehicle data. In addition, while evaluating the importance ofdata, the mobile terminal 702 controls transmission of the data to themanagement server 704. Namely, the mobile terminal 702 uploads data tothe management server 704 via the mobile network 703 while performingdynamic timing control.

A control signal used for the data transmission control by the mobileterminal 702 is acquired simultaneously with uploading of data to themanagement server 704. In addition, the control signal is a cost map ρ[k] (k is a number of a small area), which is transmission costinformation about an individual small area in one of the squares in amap formed as a square grid.

FIG. 13 illustrates an example of an internal configuration of a controlunit 801 included in the mobile terminal 702. By causing the CPU toperform a program, the mobile terminal 702 realizes functions of thecontrol unit 801 that will be described with reference to FIG. 13.Hereinafter, an operation will be described with reference to FIG. 13.In this operation, when new determination data is inputted, itsinformation value is determined, and data transmission control isperformed. For ease of description, the following description will bemade assuming that vehicle position data determined to be determinationdata will be used as input data. However, other OBD data determined tobe the corresponding dependent data is also transmitted when the vehicleposition data is transmitted. In addition, the input data is inputtedregularly at certain time intervals and the time slots are defined bythe intervals.

The control unit 801 performs data transmission control by using inputdata (t, yt) 830, which is a combination of the current time and vehicleposition data, and a cost map 831, which is transmission costinformation obtained from the management server 704.

The control unit 801 includes a time slot operation unit 802, twoestimation units 803 and 804, an information value calculation unit 805,a dynamic uploader 806, and a cost calculation unit 807.

The time slot operation unit 802 calculates an internally managed timeslot number from the time of the input data 830 and adds the time slotnumber to the input data 830. Next, the time slot operation unit 802outputs the input data 830 to a queue 821 included in the estimationunit 803, a Kalman filter 824 included in the estimation unit 804, andthe cost calculation unit 807.

The estimation units 803 and 804 include Kalman filters 823 and 824having the same functions, respectively. The estimation units 803 and804 use the respective Kalman filters to estimate a state at a time slots. At each time slot, the estimation unit 804 receives the input data830, estimates a state, and outputs an estimated value 833. Theestimation unit 803 temporarily buffers the input data 830 in the queue821 and outputs an estimated value 832 by using data loaded last by adata loader 822.

The estimation unit 804 applies the Kalman filter 824 to the inputtedvehicle position data y[s] at a time slot s and calculates internalstate x2[s] and its variance-covariance matrix Σ2[s] by using x2[s−τ]and Σ2[s−τ] τ time slots ago (for example, τ=1) on the basis of thefollowing mathematical expressions (3) and (4).x ₂ ⁻[s]=F(τ)x ₂ ⁻[s−τ]  (3)Σ₂ ⁻[s]=F(τ)Σ₂ ⁻[s−τ]F(τ)^(T) +Q(τ)  (4)F(τ) and Q(τ) are represented by the following mathematical expressions(5) and (6). In addition, σ1 and σ2 are parameters that define the sizesof process noises and are positive real numbers.

$\begin{matrix}{{F(\tau)} = \begin{pmatrix}{1 + \tau} & 0 & {- \tau} & 0 \\0 & {1 + \tau} & 0 & {- \tau} \\\tau & 0 & {1 - \tau} & 0 \\0 & \tau & 0 & {1 - \tau}\end{pmatrix}} & (5) \\{{Q(\tau)} = \begin{pmatrix}{\rho_{1}\tau} & 0 & 0 & 0 \\0 & {\rho_{2}\tau} & 0 & 0 \\0 & 0 & 0 & 0 \\0 & 0 & 0 & 0\end{pmatrix}} & (6)\end{matrix}$

Alternatively, the internal state x2[s] and its variance-covariancematrix Σ2[s] may be calculated by using the vehicle position data y[s]on the basis of the following mathematical expressions (7) and (8).x ₂ ⁻[s]=x ₂ ⁻[s]+Σ₂ ⁻[s]H ^(T)(H Σ ₂ ⁻[s]H ^(T) +R)⁻¹(y[s]−Hx ₂⁻[s])  (7)Σ₂ ⁻[s]=(I−Σ ₂ ⁻[s]H ^(T)(H Σ ₂ ⁻[s]H ^(T) +R)⁻¹ H)Σ₂ ⁻[s]  (8)In the above mathematical expressions (7) and (8), H and R arerepresented by the following mathematical expressions (9) and (10), andI is a unit matrix.

$\begin{matrix}{H = \begin{pmatrix}1 & 0 & 0 & 0 \\0 & 1 & 0 & 0\end{pmatrix}} & (9) \\{R = \begin{pmatrix}\lambda_{1} & 0 \\0 & \lambda_{2}\end{pmatrix}} & (10)\end{matrix}$In addition, λ1 and λ2 are parameters that specify the sizes ofmeasurement noises and are positive real numbers. The internal statex2[s] and its variance-covariance matrix Σ2[s] are outputted as theestimated value 833 and held until a state at the next time slot s+1 isestimated. The internal state is a four dimension vector, and thevariance-covariance matrix is a 4×4 matrix.

The estimation unit 803 inputs new input data 830 to the queue 821 ateach time slot and holds input data in chronological order.

When the data loader 822 receives a data load command from the dynamicuploader 806, the data loader 822 inputs all the data held in the queue821 to the Kalman filter 823 without changing the order of the data. Thedata loader 822 does not perform any particular operation unless thedata loader 822 receives a data load command from the dynamic uploader806.

The Kalman filters 823 and 824 have the same configuration. However,since the Kalman filter 823 does not have the new data input, the Kalmanfilter 823 calculates an estimated value at the time slot s on the basisof an internal state x1 [s−k], which is calculated when the last datainput is made (at a time slot s-k), and its variance-covariance matrixΣ1[s−k]. The Kalman filter 823 uses the above mathematical expressions(3) and (4) and outputs the estimated value x1−[s] and Σ1−[s] as theestimated value 832. The mathematical expressions (7) and (8) are notused since the measured data y[s] is not present.

The management server 704 includes an estimation function equivalent tothe Kalman filters 823 and 824. If the same data is used for estimation,the same estimated value is obtained. Namely, even when the managementserver 704 is not provided with an estimated measured data, the mobileterminal 702 can determine the prediction and estimation accuracy of themanagement server 704.

The information value calculation unit 805 calculates the informationvalue of the input data 830 on the basis of the estimated values 832 and833 at the time slot s. The information value calculation unit 805calculates the magnitude of the information value V on the basis of theabove mathematical expression (1) in which the estimated values 832 and833 are used with a Gaussian function ΦI=N(xi[s], Σi[s])(i=1, 2). Theinformation value calculation unit 805 outputs the calculated magnitudeof the information value V to the dynamic uploader 806 as an informationvalue 835.

The cost calculation unit 807 acquires the cost map 831 from themanagement server 704 and calculates a transmission cost required fortransmission of the data as of this moment from the current positiondata.

A cost map 831 represents a cost value P (ε[0, 1]) in a small area i.The cost calculation unit 807 calculates a cost by calculating its ownsmall area i from the current position r. The cost calculation unit 807outputs the cost value P to the dynamic uploader 806 as cost information834. By giving an importance attribute “M” to the data at the latesttime slot and the data at the previous time slot, the data can be markedas important points characterized as change points. An importanceattribute “L” is given to other data.

The dynamic uploader 806 acquires the magnitude of the information valueV from the information value 835 and the cost value P from the costinformation 834. The dynamic uploader 806 calculates transmissionprobability in accordance with the above mathematical expression (2) anddetermines whether to transmit the corresponding data in accordance withthe calculated transmission probability. If the dynamic uploader 806determines to transmit the data, the dynamic uploader 806 performs datatransmission and filter synchronization.

The data transmission is processing for transmitting a set of OBD data,namely, the data (position data as determination data) accumulated inthe queue 821 and the dependent data associated with the accumulateddata, to the management server 704.

The filter synchronization is processing for synchronizing the states ofthe Kalman filters in the estimation units 803 and 804. Morespecifically, the Kalman filters are synchronized by an operation asdescribed below. First, the dynamic uploader 806 issues a data uploadcommand to the data loader 822. The data loader 822 inputs all the dataaccumulated in the queue 821 to the Kalman filter 823 in chronologicalorder. On the basis of the mathematical expressions (3) to (10), theKalman filter 823 sequentially updates the internal state and itsvariance-covariance matrix up until the time slot s and calculates theinternal state x1[s] and its variance-covariance matrix Σ1[s]. In thisoperation, the internal state x1[s] and its variance-covariance matrixΣ1[s] are caused to be the same as the internal x2[s] and itsvariance-covariance matrix Σ2[s], and the Kalman filters 823 and 824 aresynchronized in the same state.

The control unit 801 of the mobile terminal 702 repeatedly performs theabove operation to dynamically control data uploading. As a result, themobile terminal 702 preferentially transmits important data to themanagement server 704 while avoiding competition with the otherterminals.

Thus, the data collection and management system according to the secondexemplary embodiment can suitably be applied to a system in whichvehicle information such as about automobiles is collected and managed.

The above exemplary embodiments can partially or entirely be describedbut not limited to as follows.

[Mode 1]

See the data collection and management system according to the abovefirst aspect.

[Mode 2]

The data collection and management system according to mode 1; whereinthe transmission control unit preferentially transmits measured datadetermined to be more important on the basis of a predetermined policyover other measured data.

[Mode 3]

The data collection and management system according to mode 1 or 2;

wherein the terminal(s) further comprises a storage unit that storesattribute classification information used for classifying measured datainto first measured data and second measured data that is dependent onthe first measured data; and

wherein the calculation unit calculates the information value ofmeasured data classified into the first measured data.

[Mode 4]

The data collection and management system according to any one of modes1 to 3;

wherein the transmission control unit calculates transmissionprobability of measured data by performing statistical processing on theinformation value and the transmission cost information and transmitsthe measured data to the management apparatus in accordance with thetransmission probability.

[Mode 5]

The data collection and management system according to any one of modes1 to 4;

wherein, on the basis of measured data that has already been acquiredprior to first timing at which measured data, for which the informationvalue is calculated, is acquired, the calculation unit calculates afirst probability distribution of measured data at a timing at or afterthe first timing;

wherein, on the basis of measured data that has already been acquired upuntil the first timing inclusive, the measured data including themeasured data acquired at the first timing, the calculation unitcalculates a second probability distribution of measured data at atiming at or after the first timing; and

wherein the calculation unit calculates the information value ofmeasured data on the basis of a distance between the first probabilitydistribution and the second probability distribution.

[Mode 6]

The data collection and management system according to any one of modes1 to 5;

wherein the management apparatus further comprises a transmission costmanagement unit that increases a value representing the transmissioncost information as concentration degree of resources amount consumed bymeasured data transmitted by the terminal(s) increases.

[Mode 7]

The data collection and management system according to any one of modes1 to 6;

wherein the management apparatus further comprises an estimation unitthat receives measured data preferentially transmitted from theterminal(s) over other measured data and estimates measured data thathas not been received yet from the terminal(s) on the basis of thepreferentially-transmitted measured data.

[Mode 8]

The data collection and management system according to any one of modes1 to 7;

wherein the management apparatus further comprises a data managementunit that stores measured data preferentially transmitted from theterminal(s) over other measured data separately from the other measureddata and provides better access performance to thepreferentially-transmitted measured data than the other measured data.

[Mode 9]

See the data collection and management method according to the abovesecond aspect.

[Mode 10]

The data collection and management method according to mode 9; wherein,in the transmission control step, measured data determined to be moreimportant on the basis of a predetermined policy is preferentiallytransmitted over other measured data.

[Mode 11]

The data collection and management method according to mode 9 or 10;

wherein, in the transmission control step, transmission probability ofmeasured data is calculated by performing statistical processing on theinformation value and the transmission cost information and the measureddata is transmitted from the terminal(s) to the management apparatus inaccordance with the transmission probability.

[Mode 12]

See the terminal according to the above third aspect.

[Mode 13]

The terminal according to mode 12;

wherein the transmission control unit preferentially transmits measureddata determined to be more important on the basis of a predeterminedpolicy over other measured data.

[Mode 14]

The terminal according to mode 12 or 13;

wherein the terminal(s) further comprises a storage unit that storesattribute classification information used for classifying measured datainto first measured data and second measured data that is dependent onthe first measured data; and

wherein the calculation unit calculates the information value ofmeasured data classified into the first measured data.

[Mode 15]

The terminal according to any one of modes 12 to 14;

wherein the transmission control unit calculates transmissionprobability of measured data by performing statistical processing on theinformation value and the transmission cost information and transmitsthe measured data to the management apparatus in accordance with thetransmission probability.

[Mode 16]

The terminal according to any one of modes 12 to 15;

wherein, on the basis of measured data that has already been acquiredprior to first timing at which measured data, for which the informationvalue is calculated, is acquired, the calculation unit calculates afirst probability distribution of measured data at a timing at or afterthe first timing;

wherein, on the basis of measured data that has already been acquired upuntil the first timing inclusive, the measured data including themeasured data acquired at the first timing, the calculation unitcalculates a second probability distribution of measured data at atiming at or after the first timing; and

wherein the calculation unit calculates the information value ofmeasured data on the basis of a distance between the first probabilitydistribution and the second probability distribution.

[Mode 17]

A terminal control method, comprising:

causing a terminal(s) to calculate an information value indicating avalue of measured data as information; and

causing the terminal(s) to determine whether to transmit the measureddata from the terminal(s) to a management apparatus that managesmeasured data on the basis of transmission cost information indicating acost incurrable upon transmitting the measured data to the managementapparatus by the terminal(s) and the information value.

[Mode 18]

A program, causing a computer that controls a terminal(s) to perform:

calculation processing for causing the terminal(s) to calculate aninformation value indicating a value of measured data as information;and

transmission control processing for causing the terminal(s) to determinewhether to transmit the measured data from the terminal(s) to amanagement apparatus that manages measured data on the basis oftransmission cost information indicating a cost incurrable transmittingthe measured data to the management apparatus by the terminal(s) and theinformation value.

[Mode 19]

See the management apparatus according to the above fourth aspect.

[Mode 20]

The management apparatus according to mode 19, further comprising atransmission cost management unit that increases a value representingthe transmission cost information as measured data transmitted by theterminal(s) consume resources more intensively.

[Mode 21]

The management apparatus according to mode 19 or 20, further comprisingan estimation unit that receives measured data preferentiallytransmitted from the terminal(s) over other measured data and estimatesmeasured data that has not been received yet from the terminal(s) on thebasis of the preferentially-transmitted measured data.

[Mode 22]

A management apparatus control method, comprising:

causing a management apparatus to receive measured data transmitted froma terminal(s); and

causing the management apparatus to notify the terminal(s) oftransmission cost information indicating a cost incurrable upontransmitting measured data to the management apparatus by theterminal(s).

[Mode 23]

A program, causing a computer that controls a management apparatus toperform:

reception processing for causing the management apparatus to receivemeasured data transmitted from a terminal(s); and

notification processing for causing the management apparatus to notifythe terminal(s) of transmission cost information indicating a costincurrable upon transmitting measured data to the management apparatusby the terminal(s).

Each of the programs in modes 18 and 23 can be recorded in acomputer-readable storage medium. Examples of the storage medium includea non-transient storage medium such as a semiconductor memory, a harddisk, a magnetic recording medium, or an optical recording medium.

[Mode 24]

A data collection and management system in which a plurality of computerterminals collect measured data in a time-series manner and transmit themeasured data to a server via a network and the server manages thetransmitted data,

wherein the server preferentially allocates resources to the measureddata depending on the importance of the measured data and estimatesuncollected data by using collected data as needed; wherein if necessaryestimation accuracy cannot be achieved or measured data is necessary,the server collects corresponding measured data from a correspondingcomputer terminal on demand; wherein the server regularly updates orcalculates a transmission cost from a load status of the server and aprediction and estimation status of measured data and notifies acorresponding computer terminal of the transmission cost as a controlsignal; and wherein an individual one of the computer terminalscalculates information values of a plurality of data measured currently,obtains transmission cost information by using the control signalreceived from the server, determines the importance of data, anddynamically adjusts timing of transmission to the server.

[Mode 25]

The data collection and management system according to mode 24; whereininformation about the position, acceleration, temperature, or humidityis at least included in the data collected by the computer terminals.

[Mode 26]

The data collection and management system according to mode 24; whereinan individual information value is an index that quantifies themagnitude of the impact caused by presence/absence of measured data; and

wherein the information value of measured data is determined on thebasis of the magnitude of the difference between a result obtained byprediction and estimation that uses the measured data and a resultobtained by prediction and estimation that does not use the measureddata, by using a prediction and estimation apparatus using measured datain the past.

[Mode 27]

The data collection and management system according to mode 24; whereinthe transmission cost information is an index that reflects howintensely data uploaded by the terminals consumes resources; wherein thetransmission cost is increased as the resources are consumed moreintensely; and

wherein the control signal is a processed and shaped signal so that anindividual terminal that has received the control signal can calculate atransmission cost required to transmit the corresponding data.

[Mode 28]

The data collection and management system according to mode 27; whereinthe transmission cost is a cost defined by the transmission position ofthe corresponding terminal; and

wherein the control signal represents cost map information correspondingto the cost, and the cost map information transmitted to an individualterminal is about all areas or at least one area.

[Mode 29]

The data collection and management system according to mode 26; whereinthe prediction and estimation is represented by a prediction andestimation distribution in which target data could exist;

wherein means that uses a recursive filter as typified by a Kalmanfilter is used, so as to estimate a state while sequentially updating aninternal state without history information in the past; and

wherein the information value is calculated by estimating a state withtarget data and a state without the target data and by calculating thedifference in the distance between predicted and estimated datadistributions determined by both the state estimation results.

[Mode 30]

The data collection and management system according to mode 24; whereinthe transmission timing is adjusted on the basis of transmissionprobability, which is represented by a decreasing function with respectto the transmission cost calculated from the control signal and anincreasing function with respect to the information value.

[Mode 31]

The data collection and management system according to any one of modes24, 26 and 29;

wherein the server includes a prediction and estimation functionequivalent to that of the computer terminals; and

wherein, even when the server is not provided with data, an individualcomputer terminal can determine the prediction and estimation accuracyof the server.

The disclosure of each of the above PTLs is incorporated herein byreference thereto. Modifications and adjustments of the exemplaryembodiments and examples are possible within the scope of the overalldisclosure (including the claims) of the present invention and based onthe basic technical concept of the present invention. In addition,various combinations and selections of various disclosed elements(including the elements in each of the claims, exemplary embodiments,examples, drawings, etc.) are possible within the scope of the entiredisclosure of the present invention. Namely, the present invention ofcourse includes various variations and modifications that could be madeby those skilled in the art according to the entire disclosure includingthe claims and the technical concept. In particular, the presentdescription discloses numerical value ranges. However, even if thedescription does not particularly disclose arbitrary numerical values orsmall ranges included in the ranges, these values and ranges should bedeemed to have been specifically disclosed.

REFERENCE SIGNS LIST

-   1, 10, 10-1 to 10-n terminal-   2 management apparatus-   20 server-   30, 30-1 to 30-m measurement apparatus-   40 network-   101 data collection interface-   102 data structure information management unit-   103 data collection unit-   104, 205 data management unit-   105, 202 control signal management unit-   106 information value estimation unit-   107 data transmission control unit-   108, 201 communication unit-   203 data reception unit-   204 data estimation unit-   206 database-   701 vehicle-   702 mobile terminal-   703 mobile network-   704 management server-   705 sensor unit-   706 OBD interface-   801 control unit-   802 time slot operation unit-   803, 804 estimation unit-   805 information value calculation unit-   806 dynamic uploader-   807 cost calculation unit-   821 queue-   822 data loader-   823, 824 Kalman filter-   901 notification unit-   902 calculation unit-   903 transmission control unit

The invention claimed is:
 1. A data collection and management system,comprising: a terminal(s) that transmits measured data; and a managementapparatus that receives and manages the measured data transmitted fromthe terminal(s), wherein the management apparatus comprises anotification unit that notifies the terminal(s) of transmission costinformation indicating a cost incurrable upon transmitting measured datato the management apparatus by the terminal(s), wherein the terminal(s)comprises: a calculation unit that calculates an information valueindicating a value of measured data as information; and a transmissioncontrol unit that determines whether to transmit measured data to themanagement apparatus based on the information value and the transmissioncost information, wherein the transmission control unit calculatestransmission probability of measured data by performing statisticalprocessing on the information value and the transmission costinformation and transmits the measured data to the management apparatusin accordance with the transmission probability.
 2. The data collectionand management system according to claim 1; wherein the transmissioncontrol unit preferentially transmits measured data determined to beimportant based on a predetermined policy over other measured data. 3.The data collection and management system according to claim 1; whereinthe terminal(s) further comprises a storage unit that stores attributeclassification information used for classifying measured data into firstmeasured data and second measured data that is dependent on the firstmeasured data; and wherein the calculation unit calculates theinformation value of measured data classified into the first measureddata.
 4. The data collection and management system according to claim 1;wherein, based on measured data that has already been acquired prior tofirst timing at which measured data, for which the information value iscalculated, is acquired, the calculation unit calculates a firstprobability distribution of measured data at a timing at or after thefirst timing; wherein, based on measured data that has already beenacquired up until the first timing inclusive, the measured dataincluding the measured data acquired at the first timing, thecalculation unit calculates a second probability distribution ofmeasured data at a timing at or after the first timing; and wherein thecalculation unit calculates the information value of measured data basedon a distance between the first probability distribution and the secondprobability distribution.
 5. The data collection and management systemaccording to claim 1; wherein the management apparatus further comprisesa transmission cost management unit that increases a value representingthe transmission cost information as concentration degree of resourcesamount consumed by measured data transmitted by the terminal(s)increases.
 6. The data collection and management system according toclaim 1; wherein the management apparatus further comprises anestimation unit that receives measured data preferentially transmittedfrom the terminal(s) over other measured data and estimates measureddata that has not been received yet from the terminal(s) based on thepreferentially-transmitted measured data.
 7. A data collection andmanagement method, comprising: causing a terminal(s) to transmitmeasured data to a management apparatus; causing a management apparatusto notify the terminal(s) of transmission cost information indicating acost incurrable upon transmitting measured data to the managementapparatus by the terminal(s); calculating an information valueindicating a value of measured data as information; and causing theterminal(s) to determine whether to transmit measured data to themanagement apparatus based on the information value and the transmissioncost information, including calculating transmission probability ofmeasured data by performing statistical processing on the informationvalue and the transmission cost information and transmits the measureddata to the management apparatus in accordance with the transmissionprobability.
 8. A terminal, comprising: a calculation unit thatcalculates an information value indicating a value of measured data asinformation; and a transmission control unit that determines whether totransmit measured data to a management apparatus based on: transmissioncost information indicating a cost incurrable upon transmitting themeasured data by the terminal to the management apparatus that managesmeasured data; and the information value, wherein the transmissioncontrol unit calculates transmission probability of measured data byperforming statistical processing on the information value and thetransmission cost information and transmits the measured data to themanagement apparatus in accordance with the transmission probability. 9.A management apparatus, comprising: a reception unit that receivesmeasured data transmitted from a terminal(s); and a notification unitthat notifies the terminal(s) of transmission cost informationindicating a cost incurrable upon transmitting measured data to themanagement apparatus by the terminal(s), wherein the terminal(s)calculate transmission probability of measured data by performingstatistical processing on the information value and the transmissioncost information and transmits the measured data to the managementapparatus in accordance with the transmission probability.